The overall goals of this project are (l) to support and extend our previous work on primate colony management methods, and (2) to make a major expansion of these efforts into statistical genetic analysis, an exciting area that promises to contribute to the understanding of problems in physiology, morphology and behavior in animal colonies, where much of the traditional research has not included genetic factors. There are impediments to the widespread use of these methods, because they are unfamiliar, technically complex, and difficult to learn in isolation. We propose (a) to develop new programs and procedures, and to tailor existing software that will assist scientists in the application of these methods, (b)to provide consulting and an opportunity for hands-on experience that will enable scientists to learn to use these methods for analysis of animal data, and (c) to distribute our analysis software to users at other institutions. The project will provide continuing support for colony managers who use our software for colony management and analysis, with particular emphasis on those who wish to develop and manage their colonies in such a way as to maximize their animals' suitability for statistical genetic analysis. It will afford opportunities for scientists who work with animals to spend time working with a team of statistical geneticists in the Population Genetics and Quantitative Genetics Laboratories at the Southwest Foundation, who have made pioneering developments in the application of statistical genetic analyses to animal populations. The project will also involve development of new software that will aid in preparing data for statistical genetic analysis (subdivision of pedigrees, PAP model preparation, defining kin relationships for subsets of animals). It will provide expanded simulation modeling of quantitative traits for animal colonies, and provide external consulting and distribution of software and documentation to investigators who have spent time working with the geneticists in our laboratories. Our experience has been that statistical genetic methods are extraordinarily valuable in contributing to the understanding of basic biological processes. We are convinced that they deserve wider use, and that despite their complexity, they can be made accessible to a wider range of users, given the appropriate environment. We believe that our laboratories can provide just this environment, and in fact, our team of statistical geneticists is eager to participate in this effort.